Genomic selection (GS) is a breeding approach which exploits genome-wide information and whose unprecedented success has shaped several animal and plant breeding schemes through delivering their genetic progress. This is the first study assessing the potential of GS in apricot (Prunus armeniaca) to enhance postharvest fruit quality attributes. Genomic predictions were based on a F1 pseudo-testcross population, comprising 153 individuals with contrasting fruit quality traits. They were phenotyped for physical and biochemical fruit metrics in contrasting climatic conditions over two years. Prediction accuracy (PA) varied from 0.31 for glucose content with the Bayesian LASSO (BL) to 0.78 for ethylene production with RR-BLUP, which yielded the most accurate predictions in comparison to Bayesian models and only 10% out of 61,030 SNPs were sufficient to reach accurate predictions. Useful insights were provided on the genetic architecture of apricot fruit quality whose integration in prediction models improved their performance, notably for traits governed by major QTL. Furthermore, multivariate modeling yielded promising outcomes in terms of PA within training partitions partially phenotyped for target traits. This provides a useful framework for the implementation of indirect selection based on easy-to-measure traits. Thus, we highlighted the main levers to take into account for the implementation of GS for fruit quality in apricot, but also to improve the genetic gain in perennial species.

基因组选择（GS）是一种利用全基因组信息的育种方法，其空前的成功通过提供其遗传进展而塑造了几种动植物育种方案。这是第一项评估GS在杏子中的潜力的研究（Prunus armeniaca）），以增强收获后果实的品质属性。基因组预测基于F1伪测试杂交种群，其中包括153个具有相反果实品质性状的个体。在两年的不同气候条件下，对它们的物理和生化水果指标进行了表型分析。预测准确性（PA）从使用贝叶斯LASSO（BL）的葡萄糖含量的0.31到使用RR-BLUP进行乙烯生产的0.78的变化，与贝叶斯模型相比，预测的准确性最高，在61,030个SNP中只有10％足以达到准确的预测。对杏果实品质的遗传结构提供了有用的见解，将其整合到预测模型中可以改善其性能，特别是对于主要QTL控制的性状。此外，多变量建模在针对目标性状部分表型化的训练分区中的PA方面产生了可喜的结果。这为基于易于测量的特征实现间接选择提供了有用的框架。因此，我们强调了主要手段，既要考虑使用GS来提高杏果实品质，又要改善多年生物种的遗传增益。